






import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__a:     List[str]   					=					logging.get_logger(__name__)




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						Union[str, Any]:
       _UpperCAmelCase							=    UniSpeechSatForSequenceClassification.from_pretrained(__snake_case   ,      config=__snake_case )
       _UpperCAmelCase							=    downstream_dict["""projector.weight"""]
       _UpperCAmelCase							=    downstream_dict["""projector.bias"""]
       _UpperCAmelCase							=    downstream_dict["""model.post_net.linear.weight"""]
       _UpperCAmelCase							=    downstream_dict["""model.post_net.linear.bias"""]
       return model




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						List[str]:
       _UpperCAmelCase							=    UniSpeechSatForAudioFrameClassification.from_pretrained(__snake_case   ,      config=__snake_case )
       _UpperCAmelCase							=    downstream_dict["""model.linear.weight"""]
       _UpperCAmelCase							=    downstream_dict["""model.linear.bias"""]
       return model




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case )				->						Union[str, Any]:
       _UpperCAmelCase							=    UniSpeechSatForXVector.from_pretrained(__snake_case   ,      config=__snake_case )
       _UpperCAmelCase							=    downstream_dict["""connector.weight"""]
       _UpperCAmelCase							=    downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
              _UpperCAmelCase							=    downstream_dict[
                  f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
              ]
              _UpperCAmelCase							=    downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

       _UpperCAmelCase							=    downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
       _UpperCAmelCase							=    downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
       _UpperCAmelCase							=    downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
       _UpperCAmelCase							=    downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
       _UpperCAmelCase							=    downstream_dict["""objective.W"""]
       return model




@torch.no_grad()
def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )				->						Tuple:
       _UpperCAmelCase							=    torch.load(__snake_case   ,      map_location="""cpu""" )

       _UpperCAmelCase							=    checkpoint["""Downstream"""]

       _UpperCAmelCase							=    UniSpeechSatConfig.from_pretrained(__snake_case )
       _UpperCAmelCase							=    WavaVecaFeatureExtractor.from_pretrained(
           __snake_case   ,      return_attention_mask=__snake_case   ,      do_normalize=__snake_case )

       _UpperCAmelCase							=    hf_config.architectures[0]
       if arch.endswith("""ForSequenceClassification""" ):
              _UpperCAmelCase							=    convert_classification(__snake_case   ,      __snake_case   ,      __snake_case )
       elif arch.endswith("""ForAudioFrameClassification""" ):
              _UpperCAmelCase							=    convert_diarization(__snake_case   ,      __snake_case   ,      __snake_case )
       elif arch.endswith("""ForXVector""" ):
              _UpperCAmelCase							=    convert_xvector(__snake_case   ,      __snake_case   ,      __snake_case )
       else:
              raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}""" )

       if hf_config.use_weighted_layer_sum:
              _UpperCAmelCase							=    checkpoint["""Featurizer"""]["""weights"""]

       hf_feature_extractor.save_pretrained(__snake_case )
       hf_model.save_pretrained(__snake_case )


if __name__ == "__main__":
  __a:     Any   					=					argparse.ArgumentParser()
  parser.add_argument(
      '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
  )
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
  parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
  parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
  __a:     Optional[Any]   					=					parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

import math

def a__       (			A_   ):






  '''simple docstring'''





  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5,	int(math.sqrt(A_   ) + 1   ),	6   ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True

def a__       (			A_ = 0.1   ):






  '''simple docstring'''





  __magic_name__								=			3
  __magic_name__								=			3

  while primes / (2 * j - 1) >= ratio:
    for i in range(j * j + j + 1,	(j + 2) * (j + 2),	j + 1   ):
      primes += is_prime(A_   )
    j += 2
  return j


if __name__ == "__main__":
    import doctest

    doctest.testmod()

